Quantifying the national responsibilities for the conservation of transboundary migratory species Siberian ibex
Bibliographic record
Abstract
Transboundary conservation is critical to halting global biodiversity loss, yet transboundary species represent a particular challenge in terms of who is primarily responsible for their conservation and how to coordinate between range states. Based on intensive field surveys from 2010 to 2023 and systematic data compilation from multilingual literature, we developed the first quantitative framework for assessing national conservation responsibilities for Siberian ibex ( Capra sibirica ) across its 11-country distribution. We integrated ensemble species distribution models with systematic conservation planning to identify 48 Landscape Conservation Units (LCUs), then applied an entropy weight method to quantify each country’s conservation responsibility based on ecological importance, protection effectiveness, and national capacity. Our assessment reveals a three-tier classification: high-responsibility countries (China, Turkmenistan, Mongolia), medium-responsibility countries (Russia, Kazakhstan, Tajikistan, India), and low-responsibility countries (Kyrgyzstan, Pakistan, Afghanistan, Uzbekistan). China holds the highest responsibility (score = 0.678) due to its extensive LCUs (45.68 % of total LCUs) and high national capacity, though its low protected area coverage (15.18 %) indicates urgent need for conservation investment. While climate change threatens future habitat availability, anthropogenic pressures from infrastructure development and inadequate protection networks pose more immediate challenges. Our responsibility assessment framework provides a replicable protocol for other transboundary migratory species, helping countries work together more fairly and effectively to prevent extinctions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".